Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Offline Reinforcement Learning with Differential Privacy
Authors: Dan Qiao, Yu-Xiang Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform numerical simulations to evaluate and compare the performance of our algorithm DP-VAPVI (Algorithm 2) with its non-private counterpart VAPVI [Yin et al., 2022] as well as a popular baseline PEVI [Jin et al., 2021]. The results complement the theoretical findings by demonstrating the practicality of DP-VAPVI under strong privacy parameters. |
| Researcher Affiliation | Academia | Dan Qiao Department of Computer Science UC Santa Barbara Santa Barbara, CA 93106 EMAIL Yu-Xiang Wang Department of Computer Science UC Santa Barbara Santa Barbara, CA 93106 EMAIL |
| Pseudocode | Yes | Algorithm 1 Differentially Private Adaptive Pessimistic Value Iteration (DP-APVI) Algorithm 2 Differentially Private Variance-Aware Pessimistic Value Iteration (DP-VAPVI) |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate DP-VAPVI (Algorithm 2) on a synthetic linear MDP example that originates from the linear MDP in [Min et al., 2021, Yin et al., 2022] but with some modifications.11 For details of the linear MDP setting, please refer to Appendix F. |
| Dataset Splits | Yes | For the offline dataset, we divide it into two independent parts with equal length: D = {(sτ h, aτ h, rτ h, sτ h+1)}h [H] τ [K] and D = {( sτ h, aτ h, rτ h, sτ h+1)}h [H] τ [K]. One for estimating variance and the other for calculating Q-values. |
| Hardware Specification | No | The paper does not mention any specific hardware (e.g., GPU/CPU models, memory) used to run the simulations. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or solver names with versions) used for the simulations. |
| Experiment Setup | Yes | The two MDP instances we use both have horizon H = 20. The number of episodes takes value from 5 to 1000. For details of the linear MDP setting, please refer to Appendix F. (In Appendix F: The linear MDP example we use consists of |S| = 2 states and |A| = 100 actions, while the feature dimension d = 10. ... The behavior policy is to always choose action a = 0 with probability p, and other actions uniformly with probability (1 p)/99. Here we choose p = 0.6.) |